1. Field of the Invention
The present invention relates to image processing, and more particularly, to an image interpolation method and apparatus.
2. Description of the Related Art
Most digital cameras and camcorders can capture color images by using a color charge-coupled device (CCD) or complimentary metal oxide semiconductor (CMOS). Captured images are images mosaicked according to the pattern of a color filter array (hereinafter, referred to as a ‘CFA’). Pixels of the mosaicked images each have two unknown color values and one known color value. The CFA pattern is formed of red (R), green (G), and blue (B) channels, and each channel is decimated according to other factors. In some implementations, the number of pixels in the decimated G channel is twice the number of pixels in the decimated R channel and twice the number of pixels in the decimated B channel. R/B pixels alternate with G pixels across a column or row. In other words, G and R pixels alternate, or G and B pixels alternate across a row or column.
Demosaicing, that is, CFA interpolation, is a process of reconstructing unknown R, G, and B components in order to generate a full-color image. Two unknown color values of each pixel may be predicted using any of various demosaicking algorithms. In demosaicking algorithms, a full-color image is reconstructed from incomplete data, namely, pixels in which two color values are missing. Such reconstruction algorithms use inter-channel or within-channel correlation of available data in R, G, and B channels.
A conventional adaptive demosaicing algorithm considers a color correlation and local characteristics of an image. Edge-directed interpolation and various adaptive weighted summation interpolation methods use an adaptive weight in order to interpolate unknown pixel values. Since a high correlation between inter-channel color differences, namely, color differences between R and G channels and between B and G channels, exists in a high-frequency region of a color image, conventional demosaicing algorithms may use this correlation.
Conventional adaptive demosaicing algorithms are introduced by X. Li [“Demosaicing by Successive Approximation,” IEEE Trans. Image Process., vol. 14, no. 3, pp. 370-379, March 2005], and by W. Lu [“Color Filter Array Demosaicing: New Method and Performance Measures”, IEEE Trans. Image Process., vol. 12, no. 10, pp. 1194-1210, October 2003].
Li's algorithm updates initially-interpolated pixel values by initially interpolating color channels and detecting an edge direction by using a value to which a Laplacian filter is applied in a color difference domain corresponding to a calculated color difference between two different initially-interpolated color channels. Updating is repeated until a stop criterion is satisfied, so that generations of false color and zipper flaws are prevented. To do this, estimated values of unknown color pixel values are adjusted by emphasizing a color difference rule in every repetition.
Lu's algorithm is an edge sensing algorithm using a color difference model or a color-ratio model. In Lu's algorithm, edges are maintained by interpolation along the edges instead of interpolation across the edges. In the method, to estimate unknown color pixel values, a spatial correlation between adjacent pixels is considered, and a suitable interpolation direction is determined using the adjacent pixels.
The above-described conventional demosaicing algorithms produce high-quality demosaiced images, and are particularly effective in reconstructing high-frequency regions of a color image, such as sharp edges. However, Li's algorithm has a low calculation speed because initially-interpolated pixel updates are repeated until a stop criterion is satisfied. In addition, Li's algorithm repeatedly changes an initially-interpolated pixel value and thus a wrong edge direction may be detected.
Moreover, the above-described conventional demosaicing algorithms do not sufficiently reduce artifacts such as a zipper effect, false color, and aliasing. A false color around edges and texturing of a demosaiced image causes the quality of image to be degraded. Overshoot flaws, such as the zipper effect and sharp color edges of a demosaiced image, are disadvantages of an adaptive algorithm.